1. Field of the Invention
The present invention relates to a method and to a system intended for real-time estimation of the flow mode of a multiphase fluid stream at all points of a pipe, comprising using neural networks.
2. Description of the Prior Art
Transporting hydrocarbons from production sites to treating plants constitutes an important link in the petroleum chain. It is a delicate link because of the complex interactions between the phases forming the transported effluents. The basic objective for operators is to reach an optimum productivity under the best safety conditions. The operator therefore have to control as best they can the velocity and the temperature so as to avoid unnecessary pressure drops, unwanted deposits and unsteady flows. The method that is generally used is to model in the best possible way the transportation of complex multiphase streams so as to provide at all times an image of the flows in the various parts of the production chain, by taking into account the precise constitution of the effluent, the flow rates, the pressures and the flow modes.
There are currently various software tools for simulating the transport of complex multiphase streams, allowing the design of suitable production equipments at an early stage.
U.S. Pat. No. 5,550,781, French Patent 2,756,004 (U.S. Pat. No. 6,028,992) and French Patent 2,756,045 (U.S. Pat. No. 5,960,187) filed by the assignee notably describe modelling methods and tools allowing simulation of the transport of complex multiphase streams for steady of transient flow and capable of accounting for instability phenomena that occur because of the irregular geometry of the formation crossed by the pipe or of the topography thereof, which is referred to by specialists as “terrain slugging” or “severe slugging”.
The simulation tools are as complex as the modelled phenomena. Precision and performance can only be obtained after a relatively long modelling time, which is not really compatible with real-time management. That is the reason why these modelling tools cannot be used as they are for real-time management of the production. It therefore appears necessary to use modelling methods offering a good compromise between calculating speed and accuracy of results.
Neural networks define a data processing mode simulating the functioning of biological neuron systems. In such networks, an element carries out a relatively simple calculation such as a weighted sum of the signals present at its inputs applied to a non-linear function, which determines the state of its output. A large number of such elements interconnected in series and in parallel is used. Proper selection of the weighting factors allows the network to carry out complex functions. Networks known as retropropagation networks for example use multiple layers of elements defined above. Adaptation of such a network to a precise task is done by “training” the network with a certain number of examples and by adjusting the weighting factors for each element to the suitable values. Input values are presented to the network, the output value produced by the network is analyzed and the weighting factors are modified to best minimize the difference between the effective value at the output and the expected value in the selected example. After a sufficient training period, the network is sulted to respond to new input values for which the output value is not known a priorl and to produce a sultable output value. In its principle, a neural network works according to a non-linear regression method which is all the more effective in relation to conventional methods.
Such networks are used in many fields such as image recognition, solution of optimization problems, etc. initially, the neural network is a method suited for automatic classification, hence its use in particular for pattern recognition. For these applications, two types of networks can be used, the MLP (Multi Layer Perceptron) or the Kohonen networks, well-known to specialists.
The prior art in the field of neural networks is illustrated by the following references:                Dreyfus G., <<Les réseaux de neurones>>; Mécanique Industrielle et Matérlaux, n 51, September 98,        Lippman R.P., An Introduction to Computing with Neural Nets; IEEE ASSP Magazine, April 1987 or        Pinkus A., Approximation Theory of the MLP Model in Neural Networks; Acta Numerica 1999.        
Networks are now also used for non-linear modelling of static data or of dynamic processes. The MLP networks are mostly used in this case. This approach currently concerns fields of application such as, for example, anomaly detection or stock-exchange prediction.
An example of use of neural networks is described for example in French Patent 2,786,568 filed by the assignee.